Systems and methods for learning new trained concepts used to retrieve content relevant to the concepts learned

ABSTRACT

A system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items. The one or more hardware processors may be further configured to obtain an indication conveying a concept to be learned from the one or more digital media items. The one or more hardware processors may be further configured to receive feedback associated with individual ones of the one or more digital media items. The one or more hardware processors may be configured to obtain individual neural network representations for the individual ones of the one or more digital media items. The one or more hardware processors may be configured to determine a trained concept based on the feedback and the neural network representations of the one or more digital media items.

FIELD OF THE DISCLOSURE

The present invention relates to systems and methods for learning newtrained concepts used to retrieve content relevant to the conceptslearned.

BACKGROUND

Digital media is frequently created by users in the forms of photos,videos, written text, and/or other forms of digital media. Thisinformation or content is generally stored, shared, accessed, and/oranalyzed by systems throughout the world. Users rely on these systems tosearch large quantities of information, explore the information, andshare it

Information used to expose this information is typically either providedby other users or created by automated systems generally using a fixedset of recognizable patterns. An example of these patterns may be a dogin a picture, the genre of music in a song, or the sentiment of asnippet of text. However, even with sophisticated systems able torecognize patterns, these systems may fail to meet the expectations ofusers who have specific criteria in mind for retrieving the content theydesire.

SUMMARY

Exemplary implementations of the disclosure may overcome theshortcomings of existing systems by facilitating the learning of newclassification systems for discriminating information and content ofinterest from other content contained in a storage system or datastream.

One or more aspects of the disclosure relate to a system configured forlearning new trained concepts used to retrieve content relevant to theconcepts learned. The system may comprise one or more hardwareprocessors configured by machine-readable instructions to obtain one ormore digital media items; obtain an indication conveying a concept to belearned from the one or more digital media items; receive feedbackassociated with individual ones of the one or more digital media items.The feedback may be based on selection of one or more positive examplesof the concept to be learned from the one or more digital media items,selection of one or more negative examples of the concept to be learnedfrom the one or more digital media items, and/or a combination thereof.A given positive example may be a digital media item, comprising theconcept to be learned. A given negative example may be a digital mediaitem, lacking the concept to be learned. The one or more hardwareprocessors are further configured by machine-readable instructions toobtain individual representations for the individual ones of the one ormore digital media items. The individual representations may include oneor more of machine learning representations, training systemsrepresentations, neural network representations, and/or other computingplatform representations for the individual ones of the one or moredigital media items. In some implementations, the one or more hardwareprocessors may be further configured by machine-readable instructions todetermine a trained concept based on the feedback, the individualrepresentations of the one or more digital media items, and/or acombination thereof. The trained concept may be usable for retrievingdigital media items relevant to the concept to be learned.

One or more aspects of the disclosure relate to a method for learningnew trained concepts used to retrieve content relevant to the conceptslearned with a system comprising one or more hardware processors. Themethod may include obtaining, one or more digital media items; obtainingan indication conveying a concept to be learned from the one or moredigital media items; and receiving feedback associated with individualones of the one or more digital media items. The feedback may be basedon selection of one or more positive examples of the concept to belearned from the one or more digital media items, selection of one ormore negative examples of the concept to be learned from the one or moredigital media items, and/or a combination thereof. In someimplementations, a given positive example may include one or more of adigital media item comprising the concept to be learned, a digital mediaitem related to the concept to be learned, a digital media item similarto the concept to be learned, a digital media item similar to otherdigital media items comprising the concept to be known, and/or otherpositive examples of the concept to be learned. Negative examples mayinclude digital media items not comprising, not related, and/or notrelevant to the concept to be learned. In some implementations, digitalmedia items may be clustered by similarity. The clusters of digitalmedia items may be labeled as positive examples or negative examples. Insome implementations, the method may further include obtainingindividual neural network representations for the individual ones of theone or more digital media items. A given neural network representationmay include one or more neural network layers. The method may furtherinclude determining a trained concept based on the feedback and theneural network representations of the one or more digital media items.The trained concept may be usable for retrieving digital media itemsrelevant to the concept to be learned.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular forms of “a”, “an”,and “the” include plural referents, unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for learning new trained conceptsused to retrieve content relevant to the concepts learned, in accordancewith one or more implementations.

FIG. 2 illustrates an electronic storage system, in accordance with oneor more implementations.

FIG. 3 illustrates a system configured for learning new concepts basedon information and feedback from a computing platform, in accordancewith one or more implementations.

FIG. 4 illustrates a system configured for learning new trainedconcepts, in accordance with one or more implementations

FIG. 5 illustrates a method for learning new trained concepts used toretrieve content relevant to the concepts learned, in accordance withone or more implementations.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 configured for learning new trainedconcepts used to retrieve content relevant to the concepts learned, inaccordance with one or more implementations. In some implementations, asshown in this example, system 100 may include one or more of servers102, one or more computing platforms 130, one or more external resources140, and/or other components.

Server(s) 102 may include electronic storage 104, one or more processors106, and/or other components. Processor(s) 106 may be configured bymachine-readable instructions 107. The machine-readable instructions 107may include one or more of a digital media component 108, a conceptcomponent 110, a feedback component 112, a machine learning component114, a training component 116, and/or other components.

Digital media component 108 may be configured to obtain one or moredigital media items. Digital media items may be any type of content thatexists in the form of digital information. For example, a given digitalmedia item may include an image, a video, text, audio, a symbol, asequence, web content, and/or any type of digital information. In someimplementations, digital media component 108 may be configured to obtaininformation associated with individual ones of the digital media items.In some implementations, information associated with individual ones ofthe digital media items may comprise information stored as metadataassociated with the digital media items. In some implementations,metadata may include one or more of timestamps, physical location wherethe media item was generated, user-generated information,source-generated information, and/or other information. In someimplementations, metadata may be referred to as description, caption,label, tags, price, user reviews, likes, comments, followers, shares,and/or any other metadata. In some implementations, digital media itemsmay be stored on digital and/or analog storage, may be digitallybroadcast, streamed, and/or contained in computer files. In someimplementations, digital media items may be obtained from electronicstorage 104, electronic storage 134, computing platforms 130, othercomponents of system 100, and/or other components outside of system 100.

In some implementations, digital media items may be obtained fromcomputing platforms 130. In some implementations, computing platforms130 may be associated with one or more of a human user, an automatedmachine, and/or other source. By way of non-limiting example, a user mayprovide a digital media item by “uploading” the digital media item,“downloading” the digital media item, and/or other ways for providing adigital media item to digital media item component 108 from computingplatforms 130. In some implementations, a user providing one or moredigital media items using a user interface 132. In some implementations,computing platforms 130 may include one or more digital media inputdevices included with, or coupled to, computing platforms 130. By way ofnon-limiting example, a media input device may include one or more of adocument scanner, a camera, a microphone, a port configured to becommunicatively coupled to a mobile device, and/or other considerations.In some implementations, electronic storage 104 and/or electronicstorage 134 may receive a query from a user via computer platforms 130to provide digital media items to digital media item component 108.

FIG. 2 illustrates an example of a storage system 200, in accordancewith one or more implementations. Storage system 200 may be configuredfor facilitating the storage of digital media items. Storage system 200may be configured for supporting queries. In some implementations,system 200 may be configured for supporting queries based on informationassociated with the digital media items. In the example illustrated inFIG. 2, digital media items may be stored in electronic storage 210. Insome implementations, digital media items may be stored on remotedevices and electronic storage 210 may contain references to the actuallocation of the digital media items (e.g., file points, urls, and/orother forms of referencing digital media).

In some implementations, information included in storage system 200 maybe modified by modification 240. Modification 240 may include one ormore of addition of new digital media items, removal of digital mediaitems, modification of information associated with the digital mediaitems stored in electronic storage 210, and/or other modification ofcontent included within storage system 200 or outside of system 200. Forexample, in some implementations, modification 240 may includemodification of digital media items stored on remote storage devices andreferenced by electronic storage 210.

In some implementations, system 200 may include an index 230 forfacilitating efficient queries and retrieval of items within electronicstorage 210. The index 230 may be configured to facilitate matchingdigital media items with corresponding information. In someimplementations, index 230 may facilitate direct lookup of contentwithin electronic storage 210 in an efficient way. By way ofnon-limiting example, indexing may be made efficient by improving one ormore of memory usage, time to index, time to query, and/or any othermetrics improvements. A query 250 to retrieve digital media items may bemade to storage system 200. A response 260 may be returned.

In some implementations, a query 250 may consist of a request forretrieval of information from storage system 200. In someimplementations, a query 250 may be a request to retrieve one or moredigital media items matching corresponding information in the request.In some implementations, a query 250 may be made by a user via computingplatforms 130 (FIG. 1) and/or other components within or outside system100 (FIG. 1). In some implementations, query 250 may be made by one ormore components within or outside of system 100 (FIG. 1). In someimplementations, query 250 may include additional operations on the oneor more digital items retrieved as a response to query 250. Additionalactions may include updating, inserting, deleting, and/or other actionson the one or more digital media items and/or actions on the informationassociated with the one or more digital media items retrieved as aresponse to query 250

In some implementations, a transformation 220 may be applied to thedigital media items to create a representation of the digital mediaitems, such representation may facilitate the efficiency indexing withindex 230. For example, in some implementations, transformation 220 maybe configured to process a digital media item, with a machine learningsystem to extract a representation, of the digital media item, includingrelevant information for efficiently indexing the digital media item inindex 230.

In some implementations, a response 260 may be retrieved responsive to aquery 250. Response 260 may convey the information requested by query250. In some implementations, response 260 may include one or moredigital media items requested by query 250. Response 260 may beretrieved by way of comparison between representations of digital mediaitems in index 230 with a representation included in query 250. Forexample, a representation of digital media items may be provided inquery 250, as an embedding into a representation space such thatcomparing embedding of all digital media items in the index 230 canyield a similarity score between the query and the indexed items. Query250 may ask for the most similar items according to the similarityscore, the most dissimilar, and/or any other combination of items toretrieve in response 260. In some implementations, digital media itemsrepresentation may be produced from one or more of a neural network,other machine learning systems, and/or other computing platforms.

Returning to FIG. 1, concept component 110 may be configured to obtainan indication conveying a concept to be learned from the one or moredigital media items. A concept may refer to one or more of information,an idea, a notion, and/or any understanding that can be learned from oneor more digital media items. In some implementations, a concept mayrefer to a sentiment, an adjective, a verb, a noun, an abstract notion,a concrete notion, and/or any other information that can be learned. Forexample, the concept of a “dog” may be learned from one or more photosthat may contain a dog in them, from one or more sounds of dogs, fromone or more descriptions of dogs, and/or from other information relatedto dogs. By way of non-limiting example, the concept “happy” may belearned from one or more images depicting happy people, from one or moresounds, from one or more audios of laughter, from one or more audios ofmusic, from one or more audios of songs, from one or more texts, and/orother information related to “happy”. An indication, conveying theconcept to be learned, may be in the form of one or more of text, sound,voice, picture, and/or other forms for conveying the concept to belearned. The indication, conveying the concept to be learned, may beobtained from computing platform(s) 130, a user, and/or other componentswithin or outside system 100.

Feedback component 112 may be configured to receive feedback associatedwith individual ones of the one or more digital media items. Feedbackreceived by feedback component 112 may be received from one or morecomponents within or outside of system 100. In some implementations, thefeedback may be received from computing platforms 130. Computingplatforms 130 may be configured to provide feedback associated withindividual ones of the one or more digital media items to feedbackcomponent 112 based on a user input. In some implementations, receivingfeedback for individual ones of the digital media items may includeobtaining confirmation of information associated with the individualones of the digital media items. In some implementations receivingfeedback for individual ones of the digital media items may includeobtaining confirmation of previous predictions related to the concept tobe learned.

In some implementations, feedback provided to feedback component 112 maybe based on selection of one or more positive examples of the concept tobe learned from the one or more digital media items, such that a givenpositive example may be a digital media item related to the concept tobe learned. The selection may be obtained from a user via user interface132 and/or one or more components within or outside system 100. In someimplementations, feedback provided to feedback component 112 may bebased on selection of one or more negative examples of the concept to belearned from the one or more digital media items such that a givennegative example may be a digital media item not related to the conceptto be learned. By way of non-limiting example, positive examples of theconcept of a “Labrador Retriever” may include images of a Labradorretriever. Negative examples of the concept of a “Labrador Retriever”may include images of cars, trees, mountains, Rottweilers, Poodles,and/or other images, which do not include a Labrador retriever. In someimplementations, feedback provided to feedback component 112 may bebased on selection of one or more positive examples and one or morenegative examples of the concept to be learned.

Machine learning component 114 may be configured to obtain individualmachine learning representations for the individual ones of the one ormore digital media items. In some implementations, the machine learningrepresentations may be neural network representations for the individualones of the one or more digital media items. A neural network istypically organized in layers made up of a number of interconnected‘nodes’ which contain an ‘activation’ function. Digital media items maybe presented to the network via an ‘input layer’, which communicates toone or more ‘hidden layers’ where the actual processing is done via asystem of weighted ‘connections’. The hidden layers then link to an‘output layer’ where a prediction is output. A given neural networkrepresentation may include one or more neural network layers. By way ofnon-limiting example, a convolutional neural network may be used forprocessing images. A convolutional neural network may be comprised ofone or more convolutional neural network layers to learn weights sharedover an image, one of more pooling layers to make the neural networkmore invariant, one or more non-linear activation function layers tomake the network more robust and/or other type of neural network layerswhile taking in the image as input and outputting a vectorrepresentation, encoding high level information extracted from theimage. By way of non-limiting example, for text, audio, and/or timeseries processing a recurrent neural network may be used to encode timeseries information as a vector output using weighted connections throughtime to process the time sequence of inputs

Training component 116 may be configured to determine one or moretrained concepts. The trained concept may be used for retrieving digitalmedia items relevant to the concept to be learned. In someimplementations, training component 116 may be configured to determine atrained concept based on one or more of the feedback associated with oneor more digital media items, the one or more digital media items, and/orthe individual representations of the one or more digital media items.In some implementations, training component 116 may be configured todetermine additional neural network layers based on the feedbackassociated with one or more digital media items, and/or the neuralnetwork representations of the one or more digital media items.

FIG. 3 illustrates a system 300 configured for learning new conceptsbased on information and feedback from a computing platform 310, inaccordance with one or more implementations. Digital media items,included in electronic storage system 330, may be modified bymodification 340. Modification 340 may include one or more of additionof new digital media items, removal of digital media items, modificationof information associated with the digital media items stored inelectronic storage 330, and/or other modification to content stored instorage system 330. Digital media items may be retrieved from storagesystem 330 and presented to computing platform 310. In someimplementations, computing platform 310 may include a user interface.

Feedback 320 associated with one or more digital media items may bereceived from computing platform 310. In some implementations, feedback320 may be conveyed in the form of selection of digital media items,selection of labels, selection of text, association of digital mediaitems with information relevant to the concept to learned, and/or otherforms of feedback. For example, a concept to be learned is “dog”.Feedback 320 associated with a set of images may be in the form of a setof selected images showing a dog, a set of selected images not showing adog. In some implementations, feedback 320 may include confirmation ofprevious predictions by system 300, labeling digital media items asbelonging to positive examples of the concept to be learned, labelingdigital media items as belonging to negative examples of the concept tobe learned, and/ or other information relevant to the concept to belearned, In some implementations, feedback 320 may be based on userinteractions with system 300. Examples of interactions with system 300may include one or more of search history, search click through rate,liking content, sharing content, commenting on content, subscribing tocontent, and/or other interactions with system 300.

In some implementations, training component 350 may be configured toretrieve digital media items from electronic storage 330, for whichfeedback 320 is provided. In some implementations, digital media itemsmay be retrieved from electronic storage 330 using a query. In someimplementations, training component 350 may include a machine learningsystem, a computing platform, and/ or a human operator(s). Trainingcomponent 350 may be configured to learn from feedback 320 and digitalmedia items retrieved from electronic storage 330. For example, trainingcomponent 350 may improve predictions of the machine learning systembased on the feedback 320 associated with the digital media items.

Training component 350 may be configured to determine a trained conceptMO. In some implementations, trained concept 360 may represent theparameters of a machine learning method used by the training component350 to learn from feedback 320. By way of non-limiting example, digitalmedia items in electronic storage 330 may be a set of images, andfeedback 320 may be labels of images containing birds and labels ofimages not containing birds. The training component 350 may learn fromthe labels provided by feedback 320. The labels provided by feedback 320may be the parameters defining a bird classifier determined by thetraining component 350.

Returning to FIG. 1, electronic storage 104 may receive queries fordigital media items related to the concept being learned. The trainedconcept obtained by training component 116 may be used to obtain resultsof the queries. In some implementations, additional feedback associatedwith the results of the queries may be received by training component116. The feedback may be based on selection of one or more positiveexamples of the concept being learned from results of the queries,and/or selection of one or more negative examples of the concept beinglearned from the results of the queries. In some implementations, thetrained concept may be adjusted based on the additional feedback. Insome implementations, machine learning component 114 may be configuredto obtain neural network representations of the results of the queries.The trained concept may be adjusted based on the additional feedbackand/or the neural network representation of the results of the queries.By way of non-limiting example, for a trained concept of a “dog”,additional feedback may include more positive examples of “dogs” (truepositives) retrieved by querying for similar items to the trainedconcept, negative examples that were not “dogs” (false positives) whenquerying for similar items to the trained concept, negative examplesthat are not “dogs” (true negatives) when querying for dissimilar itemsto the trained concept, and/or positive examples of “dogs” (falsenegatives) when querying for dissimilar items to the trained concept.

FIG. 4 illustrates a system configured for learning new trainedconcepts, in accordance with one or more implementations. In someimplementations, digital media items may be obtained by computingplatform 410. Feedback 420 associated with individual ones of thedigital media items may be generated. Feedback 420 and digital mediaitems associated with feedback 420 may be provided to training component430. Training component 430 may be configured to learn from feedback 420and digital media associated with feedback 420. A trained concept 440may be determined by training component 430. In some implementations,trained concept 440 may be transformed using a transformation 470. Thetransformation 470 may provide adjustments to the trained concept 440 tomake it suitable in a query 450 on electronic storage 408. A query 450may be used to retrieve more relevant digital media items to the conceptto be learned. The results of query 450 may be returned in a response460.

The iterative procedure shown in FIG. 4 may be used to refine thetrained concept 440 by presenting digital media items that wereretrieved using the previously trained concept 440 to the computingplatform 410 for additional feedback generation.

By way of non-limiting example, “dog” is the concept to be learned bytraining component 430. One or more images may be retrieved fromelectronic storage 408. Images representing a “dog” are provided aspositive feedback 420 and images not representing a “dog” are providedas negative feedback 420. The training component 430 may leverage amachine learning framework configured to learn from the feedback 420. Anexample machine learning framework may be a simple one-layer neuralnetwork where the trained concept 440 may be the parameters W and b ofthe neural network representing the “dog” concept. W is the weight and bis the bias of the neural network representation of the trained concept440. Electronic storage 408 may maintain a neural network representationfor individual ones of the images stored in electronic storage 408. Thetraining component 430 may be provided with the neural networkrepresentation for the images for which feedback 420 has been generated.Neural network representations of the images and the feedback 420 may beused by training component 430 to learn additional neural network layersto output as a trained concept 440. For example, the following operationmay be used to learn one additional neural network layer as the trainedconcept 440:

output=f(W*x+b)

Where W is the learned weight vector of the trained concept 440, b isthe bias of the trained concept 440, x is the neural networkrepresentation of the digital media item, and output is a confidencescore of the trained concept 440 model with f( ) being an optionalfunction applied to the output.

In some implementations, computing platform 410 may obtain moreinformation to iterate the operations described above. The trainedconcept 440 may be transformed using transformation 470 to make itsuitable to a query 450 on storage 408. A query 450 may be used toretrieve more relevant digital media items returned in a response 460.The weight W may be used in a query 450 by first applying atransformation 470 to W. A vector of weights unit length W′ may beobtained by dividing the vector W by W_(norm).

W=W/W _(norm)

To efficiently leverage unit length W of the trained concept 440, atransformation 490 may be applied on individual neural networkrepresentations in electronic storage 408. A transformation 490 mayprovide a unit length normalization x′ of the neural networkrepresentation for individual digital media items to index 480:

x′=x/x _(norm)

With transformation 490 applied, the index 480 may retrieve digitalmedia items, based on a query 450, by retrieving the nearest Euclideandistance D between the query W and vectors x′ in the index 480. Insteadof computing the distance of W to every digital media item x in theelectronic storage 408, an efficient nearest neighbor algorithm can beapplied to significantly speed up the computations.

In some implementations, the original W*x+b product of the neuralnetwork applied to individual digital media items in electronic storage408 may be retrieved. The product W*x+b may be retrieved to accuratelyorder individual digital media items when presenting to computingplatform 410. The following resealing may be applied: assuming the index480 returns the distance D of individual digital media items retrievedrelative to the normalized weight W. The W*x+b computation may beefficiently computed by using the following scalar computation:

W*x+b=−(D**2−2)*W _(norm)/(2*x _(norm))+b

After computing the W*x+b value, the order may be slightly differentfrom he order returned using Euclidean distance D returned by index 480.An additional sort may be applied on the W*x+b results if order isimportant in the returned digital media items. Since the order of Ddistances is not identical to the W*x+b outputs, a typical pattern maybe to query 450 the index 480 to retrieve a larger number of resultsthan desired, compute the W*x+b values for those returned digital mediaitems using the efficient equation above, and sort them to obtain thenumber of digital media items in a sorted order.

Returning to FIG. 1, processor(s) 106 and/or processor 136 may beconfigured to provide information processing capabilities in system 100(e.g., in server(s) 102 and/or computing platforms 130). As such,processor(s) 106 and/or processor 136 may include one or more of adigital processor, an analog processor, a digital circuit designed toprocess information, an analog circuit designed to process information,a state machine, and/or other mechanisms for electronically processinginformation. Although processor(s) 106 and/or processor 136 are shown inFIG. 1 as a single entity, this is for illustrative purposes only. Insome implementations, processor(s) 106 and/or processor 136 may includea plurality of processing units. These processing units may bephysically located within the same apparatus (e.g., in server(s) 102and/or computing platforms 130), or processor(s) 106 and/or 136 mayrepresent processing functionality of a plurality of apparatusesoperating in coordination (e.g., in server(s) 102 and/or computingplatforms 130).

Electronic storage 104, electronic storage 134, and/or electronicstorage 210 (FIG. 2) may comprise electronic storage media thatelectronically stores information. The electronic storage media ofelectronic storage 104, electronic storage 134, and or electronicstorage 210 (FIG. 2) may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or computing platforms 130, and/or may contain removable storagethat is removably connectable to server(s) 102 and/or computingplatforms 130 via, for example, a port or a drive. A port may include aUSB port, a firewire port, and/or other port. A drive may include a diskdrive and/or other drive. Electronic storage 104, electronic storage134, and/or electronic storage 210 (FIG. 2) may include one or more ofoptically-readable storage media (e.g., optical disks, etc.),magnetically-readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically-readable storage media. The electronicstorage 210 may include one or more virtual storage resources (e.g.,cloud storage, a virtual private network, and/or other virtual storageresources). Electronic storage 104, electronic storage 134, and/orelectronic storage 210 (FIG. 2) may store digital media items, softwarealgorithms, information determined by processor(s) 106 and/or processor136, information received from servers) 102 and/or computing platforms130, and/or other information that enables server(s) 102 and/orcomputing platforms 130 to function as described herein.

Computing platforms 130 may include user interface 132, electronicstorage 134, one or more of a processor 136, and/or other components.User interface 132 may be configured to provide an interface betweensystem 100 and a user through which the user may provide information toand receive information from system 100. This enables information,results and/or instructions, and any other communicable items,collectively referred to as “information”, to be communicated betweenthe user and one or more components of system 100. Examples of interfacedevices suitable for inclusion in user interface 132 include one or moreof a keypad, buttons, switches, a keyboard, knobs, levers, a displayscreen, a touch screen, speakers, a microphone, an indicator light, anaudible alarm, a printer, and/or other devices. In some implementations,user interface 132 may include a plurality of separate interfaces,including an interface that may be provided in server(s) 102, and aseparate interface provided to view and/or manage stored informationthat has been retrieved from server(s) 102 (e.g., provided by a computerconfigured to receive information from server(s) 102 and othercomponents of system 100).

The external resources 140 may include sources of information, hostsand/or providers of information outside of system 100, external entitiesparticipating with system 100, and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 140 may be provided by resources included in system100 (e.g., in server(s) 102).

The network 150 may include the Internet and/or other networks,Intranets, near field communication, frequency (RE) link, Bluetooth®,Wi-Fi, and/or any type(s) of wired or wireless network(s). It will beappreciated that this is not intended to be limiting and that the scopeof this disclosure includes implementations in which the server(s) 102,the computing platforms 130, and/or the external resources 140 areoperatively linked via some other communication media.

It should be appreciated that, although components 108, 110, 112, 114,and/or 116 are illustrated in FIG. 1 as being co-located within a singlecomponent, in implementations in which processor(s) 106 may beconfigured by machine-readable instructions 107 to execute multiplecomponents, one or more of components 108, 110, 112, and/or 114 may belocated remotely from the other components. The description of thefunctionality provided by the different components 108, 110, 112, 114and/or 116 described above is for illustrative purposes and is notintended to be limiting, as any of components 108, 110, 112, 114 and/or116 may provide more or less functionality than is described. Forexample, one or more of components 108, 110, 112, 114, and/or 116 may beeliminated, and some or all of its functionality may be provided byother ones of components 108, 110, 112, 114,116, and/or othercomponents. As another example, processor(s) 104 may be configured bymachine-readable instructions 107 to execute one or more additionalcomponents that may perform some or all of the functionality attributedbelow to one of components 108, 110, 112, 114, and/or 116.

FIG. 5 illustrates a method 500 for learning new trained concepts usedto retrieve content relevant to the concepts learned. The operations ofmethod 500 presented below are intended to be illustrative. In someembodiments, method 500 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order in which the operations of method 500are illustrated in FIG. 5 and described below is not intended to belimiting.

In some embodiments, method 500 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, a functionally-limitedprocessing device, and/or other mechanisms for electronically processinginformation). The one or more processing devices may include one or moredevices executing some or all of the operations of method 500 inresponse to instructions stored electronically on an electronic storagemedium. The one or more processing devices may include one or moredevices configured through hardware, firmware, and/or software to bespecifically designed for execution of one or more of the operations ofmethod 500.

At an operation 502, one or more digital media items may be obtained. Insome implementations, operation 502 may be performed by a digital mediacomponent the same as or similar to digital media component 108 (shownin FIG. 1 and described herein).

At an operation 504, an indication conveying a concept to be learnedfrom the one or more digital media items may be obtained. In someimplementations, operation 504 may be performed by a concept componentthe same as or similar to the concept component 110 (shown in FIG. 1 anddescribed herein).

At an operation 506, feedback associated with individual ones of the oneor more digital media items may be received. The feedback may be basedon of selection of one or more positive examples of the concept to belearned from the one or more digital media items, selection of one ormore negative examples of the concept to be learned from the one or moredigital media items, and/or a combination thereof. In someimplementations, a given positive example may be a digital media itemcomprising the concept to be learned. In some implementations, a givennegative example may be a digital media item lacking the concept to belearned. In some implementations, operation 506 may be performed by afeedback component the same as or similar to the feedback component 112(shown in FIG. 1 and described herein).

At operation 508, individual machine learning representations for theindividual ones of the one or more digital media items are obtained. Insome implementations, individual neural network representations for theindividual ones of the one or more digital media items are obtained. Insome implementations, a given neural network representation may includeone or more neural network layers. In some implementations, operation508 may be performed by a machine learning component the same as orsimilar to the machine learning component 114 (shown in FIG. 1 anddescribed herein).

At operation 510, a trained concept may be obtained. In someimplementations, the trained concept may be based on the feedback, theindividual machine learning representations of the one or more digitalmedia items, and /or a combination thereof. In some implementations,operation 510 may be performed by a training component the same as orsimilar to the training component 116 (shown in FIG. 1 and describedherein). In some implementations, the trained concept may be used forretrieving digital media items relevant to the concept to be learned.The retrieved digital media items may be used to reiterate operations502-510 described above.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

What is claimed is:
 1. A system configured for learning new trainedconcepts used to retrieve content relevant to the concepts learned, thesystem comprising: one or more hardware processors configured bymachine-readable instructions to: obtain one or more digital mediaitems; obtain an indication conveying a concept to be learned from theone or more digital media items; receive feedback associated withindividual ones of the one or more digital media items, wherein thefeedback is based on one or both of (1) selection of one or morepositive examples of the concept to be learned from the one or moredigital media items, or (2) selection of one or more negative examplesof the concept to be learned from the one or more digital media items, agiven positive example being a digital media item comprising the conceptto be learned, and a given negative example being a digital media itemlacking the concept to be learned; obtain individual neural networkrepresentations for the individual ones of the one or more digital mediaitems, a given neural network representation including one or moreneural network layers; and determine a trained concept based on (1) thefeedback and (2) the neural network representations of the one or moredigital media items, the trained concept being usable for retrievingdigital media items relevant to the concept to be learned.
 2. The systemof claim 1, wherein determining the trained concept includes determiningadditional neural network layers based on (1) the feedback and (2) theneural network representations of the one or more digital media items.3. The system of claim 1, wherein receiving feedback for individual onesof the digital media items includes obtaining confirmation ofinformation associated with the individual ones of the digital mediaitems.
 4. The system of claim 1, wherein receiving feedback forindividual ones of the digital media items includes obtainingconfirmation of previous predictions related to the concept to belearned.
 5. The system of claim 1, wherein the one or more hardwareprocessors are further configured by machine-readable instructions to:receive queries for digital media items related to the concept beinglearned; and use the trained concept to obtain results of the queries.6. The system of claim 5, wherein the one or more hardware processorsare further configured by machine-readable instructions to: receiveadditional feedback associated with the results of the queries, whereinthe feedback is based on one or both of (1) selection of one or morepositive examples of the concept being learned from results of thequeries, or (2) selection of one or more negative examples of theconcept being learned from the results of the queries.
 7. The system ofclaim 6, wherein the one or more hardware processors are furtherconfigured by machine-readable instructions to: adjust the trainedconcept based on the additional feedback and neural networkrepresentations of the results of the queries.
 8. A method for learningnew trained concepts used to retrieve content relevant to the conceptslearned with a system comprising one or more hardware processors, themethod comprising: obtaining, with the one or more hardware processors,one or more digital media items; obtaining, with the one or morehardware processors, an indication conveying a concept to be learnedfrom the one or more digital media items; receiving, with the one ormore hardware processors, feedback associated with individual ones ofthe one or more digital media items, wherein the feedback is based onone or both of (1) selection of one or more positive examples of theconcept to be learned from the one or more digital media items, or (2)selection of one or more negative examples of the concept to be learnedfrom the one or more digital media items, a given positive example beinga digital media item comprising the concept to be learned, and a givennegative example being a digital media item lacking the concept to belearned; obtaining, with the one or more hardware processors, individualneural network representations for the individual ones of the one ormore digital media items, a given neural network representationincluding one or more neural network layers; and determining, with theone or more hardware processors, a trained concept based on (1) thefeedback and (2) the neural network representations of the one or moredigital media items, the trained concept being usable for retrievingdigital media items relevant to the concept to be learned.
 9. The methodof claim 8, wherein determining the trained concept includes determiningadditional neural network layers based on (1) the feedback and (2) theneural network representations of the one or more digital media items.10. The method of claim 8, wherein receiving feedback for individualones of the digital media items includes obtaining confirmation ofinformation associated with the individual ones of the digital mediaitems.
 11. The method of claim 8, wherein receiving feedback forindividual ones of the digital media items includes obtainingconfirmation of previous predictions related to the concept to belearned.
 12. The method of claim 8, further comprising: receiving, withthe one or more hardware processors, queries for digital media itemsrelated to the concept being learned; and using, with the one or morehardware processors, the trained concept to obtain results of thequeries.
 13. The method of claim 8, further comprising: receiving, withthe one or more hardware processors, additional feedback associated withthe results of the queries, wherein the feedback is based on one or bothof (1) selection of one or more positive examples of the concept beinglearned from results of the queries, or (2) selection of one or morenegative examples of the concept being learned from the results of thequeries.
 14. The method of claim 8, further comprising: adjusting, withthe one or more hardware processors, the trained concept based on theadditional feedback and neural network representations of the results ofthe queries.